This paper presents the design and implementation of general matrix-matrix multiplication (GEMM) algorithm for the second generation Intel Xeon Phi processor codenamed Knights Landing (KNL). We illustrate several developing guidelines to achieve optimal performance with C programming language and the Advanced Vector Extensions (AVX-512) instruction set. Further, we present several environment variable issues associated with parallelization on the KNL. On a single core of the KNL, our double-precision GEMM (DGEMM) implementation achieves up to 99 percent of DGEMM performance using the Intel MKL, which is the current state-of-the-art library. Our parallel implementation for 68 cores of the KNL also achieves good scaling results, up to 93 percent of DGEMM performance using the Intel MKL.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents the design and implementation of general matrix-matrix multiplication (GEMM) algorithm for the second generation Intel Xeon Phi processor codenamed Knights Landing (KNL). We illustrate several developing guidelines to achieve optimal performance with C programming language and the Advanced Vector Extensions (AVX-512) instruction set. Further, we present several environment variable issues associated with […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,3],"tags":[1787,1782,1483,37,324,298],"class_list":["post-18273","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-intel-xeon-phi","tag-linear-algebra","tag-matrix-multiplication","tag-optimization"],"views":2401,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=18273"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18273\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=18273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=18273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=18273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}